故障診斷在現今工業中扮演重要的角色，若能對設備及早檢測和診斷便可提高系統的穩定度及可靠度，甚至可以避免突發事件的發生以省下維修所帶來的成本損失，因此故障診斷已廣泛應用於各領域，但要實現故障診斷通常需要大量的設備運轉數據來建立一個高準確率且通用於不同任務的故障診斷模型，若訓練數據不足以因應高複雜度的模型則會造成過擬合的現象影響模型的準確率，尤其馬達長期運轉於健康狀態下，使得健康狀態及故障狀態的數據失衡。
本研究致力於應用數據增強技術克服訓練數據不足及不平衡的影響。首先設計四種常見的馬達故障模型，並以一部正常馬達當成健康狀態，量測滿載、半載及無載下此五種馬達狀態之振動加速度訊號並將其轉成三維圖片，接著以在圖像辨識任務中表現良好的卷積神經網路建立故障診斷模型，最後透過兩個案例設計模擬訓練數據不足及不平衡的情形，案例一顯示當訓練數據減少至原本的20%，各負載情形下其準確率皆下降至少40%，案例二顯示當訓練數據不平衡時，各負載情形下其準確率皆未達90%，應用數據增強技術生成圖片將訓練數據增加至足夠及平衡後，兩個案例的準確率皆可提升至90%以上，證明數據增強技術應用於CNN-based馬達故障診斷模型之有效性。

Fault diagnosis plays a major role in modern industry. If faults are detected early in facilities, system stability and reliability can be improved and emergency occurrences can be prevented, thereby decreasing maintenance costs. Fault diagnosis has been practiced in a broad range of disciplines. However, much facility operation data are required to establish a fault diagnosis model that is highly accurate and applicable in different tasks. Insufficient training data sets can result in overfitting for a highly complex model, which decreases model accuracy. Motors, in particular, have long been operated in a normal state, leading to an imbalance between normal and faulty state data.
This study employed data augmentation to mitigate the adverse effects of insufficiency and imbalance in training data. Models on four common types of motor faults were established, and the performance of these modeled faulty motors were compared against that of a normal-functioning motor. The vibration acceleration signals of the five motors at full load, half load, and no load were measured and converted to 3D images. A fault diagnosis model was then constructed using a convolutional neural network, which performs well in image recognition. Finally, two cases were devised to simulate an insufficient and unbalanced training data set, respectively. In Case 1, where the training data set was only 20% the size of the original data set, the fault diagnosis accuracy decreased by at least 40% for each of the aforementioned load conditions; in Case 2, when the training data had an unbalanced number of data points, the fault diagnosis accuracy at each load condition was <90%. When this study’s data augmentation technique was applied to resolve these problems of insufficiency and imbalance, the diagnosis accuracy for both cases increased to >90%. These results confirmed that this study’s data enhancement technology can be effectively employed in a CNN-based model for motor fault diagnosis.

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